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Build a Real AI Agent with Claude and GPT

What is an AI agent?

An AI agent is not just a chatbot that answers one prompt. It is a system that can follow a goal, make a plan, choose tools, and take actions until the task is done. In practice, an agent usually has four parts: a model, instructions, tools, and a loop that lets it check its own work.

• Goal: "Triage inbound support tickets."
• Tools: Search docs, read a database, send email, create a ticket.
• Memory: Keep state about what has happened so far.
• Guardrails: Rules for when to stop, ask a human, or refuse a risky action.

The easiest way to think about it: a chatbot answers, an agent acts.

A simple build: a support triage agent

Suppose a new customer message arrives. Your agent should read it, classify urgency, look up docs, and draft a reply. If the issue is billing or account access, it can escalate instead of guessing. That is already useful, and it is small enough to build in an afternoon.

Step 1: connect your SDK to 59API. 59API is an AI API relay with pay-as-you-go pricing and native official-quality Claude and GPT models. Because it is fully compatible with Claude Code, Codex, and any OpenAI SDK, you usually only change the base URL and API key.

Python example:

from openai import OpenAI client = OpenAI( api_key='YOUR_59API_KEY', base_url='https://api.59api.com' ) response = client.chat.completions.create( model='claude-sonnet', messages=[ {'role': 'system', 'content': 'You are a support triage agent.'}, {'role': 'user', 'content': 'Customer cannot log in after password reset.'} ] ) print(response.choices[0].message.content)

Step 2: add tools. A real agent should not rely only on its own text generation. Give it small, deterministic functions such as search_docs(), lookup_order(), or create_escalation(). The model decides which tool to call, your code executes the function, and the result goes back into the conversation.

Tool pattern example:

def search_docs(query): # return the most relevant help-center articles return ['Reset your password', 'Troubleshoot login loops']

Then ask the model to answer only after it has checked the tool output. This keeps the agent grounded in your actual data instead of hallucinating support steps.

Step 3: build the loop. A minimal agent loop is: plan -> act -> observe -> repeat. After each tool call, feed the result back to the model and ask whether the task is done. Stop when it produces a final answer or when your guardrails say to escalate to a human.

For example, if the login issue is caused by a locked account, the agent can draft a reply and include the next action. If the customer mentions payment disputes or legal requests, the agent should stop and hand off immediately.

Why cost and model quality matter

Agents can make many calls in one task, so per-request cost matters fast. That is where 59API is practical: you get low-cost, pay-as-you-go access to Claude Opus, Sonnet, Haiku, Fable, and GPT models without downgrading to weaker alternatives. If you are testing prompts, building a proof of concept, or running an internal agent, cheap calls let you iterate more and waste less.

Compatibility matters too. Since 59API works with existing OpenAI-style SDKs, you can test the same agent logic in Python or Node, then move it into Claude Code or Codex workflows with minimal changes. That reduces integration time and lets your team focus on the agent design itself: tools, memory, and safety.

What to add next

• State: Save the ticket ID, user type, and tool results so the agent does not repeat work.
• Evaluation: Keep a small test set of real prompts and verify the agent’s output weekly.
• Safety: Define which requests require human review before any external action.
• Observability: Log prompts, tool calls, and final answers so you can debug failures quickly.

If you want to build your first agent without overspending, sign up for 59API and point your existing SDK at https://api.59api.com. The referral rebate is a nice bonus once your workflow starts scaling.

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